from torch import nn
from .backbone import Backbone
from .build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec

# from .utils import load_state_dict_from_url

__all__ = [
    "ConvBNReLU",
    "InvertedResidual",
    "MobileNetV2",
    "build_mobilenetv2_backbone"
]

model_urls = {
    'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class ConvBNReLU(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True)
        )


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.in_channels = inp
        self.out_channels = oup
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend([
            # dw
            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
            # pw-linear
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        ])
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV2(Backbone):
    def __init__(self,
                 num_classes=None,
                 width_mult=1.0,
                 inverted_residual_setting=None,
                 round_nearest=8,
                 block=None,
                 out_features=None):
        """
        MobileNet V2 main class

        Args:
            num_classes (None or int): if None, will not perform classification.
            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
            inverted_residual_setting: Network structure
            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
            Set to 1 to turn off rounding
            block: Module specifying inverted residual building block for mobilenet
            out_features (list[str]): name of the layers whose outputs should
                be returned in forward. If None, will return the output of the last layer.

        """
        super(MobileNetV2, self).__init__()
        self.num_classes=num_classes

        if block is None:
            block = InvertedResidual
        input_channel = 32
        last_channel = 1280

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 2],
                [6, 32, 3, 2],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
                [6, 160, 3, 2],
                [6, 320, 1, 1],
            ]

        # only check the first element, assuming user knows t,c,n,s are required
        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
            raise ValueError("inverted_residual_setting should be non-empty "
                             "or a 4-element list, got {}".format(inverted_residual_setting))

        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
        self.features_and_names = []
        self._out_feature_strides = {}
        self._out_feature_channels = {}
        conv_count = 1

        # building first layer
        current_stride = 2
        feature = ConvBNReLU(3, input_channel, stride=current_stride)
        name = "conv" + str(conv_count)
        conv_count += 1
        self.features_and_names.append((feature, name))
        self._out_feature_channels[name] = input_channel
        self._out_feature_strides[name] = current_stride

        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                feature = block(input_channel, output_channel, stride, expand_ratio=t)
                name = "conv" + str(conv_count)
                conv_count += 1
                self.features_and_names.append((feature, name))
                self._out_feature_channels[name] = feature.out_channels
                self._out_feature_strides[name] = current_stride = int(current_stride * feature.stride)
                input_channel = output_channel

        # building last several layers
        feature = ConvBNReLU(input_channel, self.last_channel, kernel_size=1)
        name = "conv" + str(conv_count)
        self.features_and_names.append((feature, name))
        self._out_feature_channels[name] = self.last_channel
        self._out_feature_strides[name] = current_stride * 1

        if out_features is None:
            out_features = [name]
        self._out_features = out_features
        assert len(self._out_features)
        # children = [x[0] for x in self.named_children()]
        # for out_feature in self._out_features:
        #     assert out_feature in children, "Available children: {}".format(", ".join(children))

        # building classifier
        if num_classes is not None:
            self.classifier = nn.Sequential(
                nn.Dropout(0.2),
                nn.Linear(self.last_channel, num_classes),
            )

        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

    # def _forward_impl(self, x):
    #     # This exists since TorchScript doesn't support inheritance, so the superclass method
    #     # (this one) needs to have a name other than `forward` that can be accessed in a subclass
    #     x = self.features(x)
    #     # Cannot use "squeeze" as batch-size can be 1 => must use reshape with x.shape[0]
    #     x = nn.functional.adaptive_avg_pool2d(x, 1).reshape(x.shape[0], -1)
    #     x = self.classifier(x)
    #     return x

    def forward(self, x):
        outputs = {}
        for feature, name in self.features_and_names:
            x = feature(x)
            if name in self._out_features:
                outputs[name] = x
        if self.num_classes is not None:
            # Cannot use "squeeze" as batch-size can be 1 => must use reshape with x.shape[0]
            x = nn.functional.adaptive_avg_pool2d(x, 1).reshape(x.shape[0], -1)
            x = self.classifier(x)
            if "linear" in self._out_features:
                outputs["linear"] = x
        return outputs

    # def size_divisibility(self):
    #     return 1

    def output_shape(self):
        """
        Returns:
            dict[str->ShapeSpec]
        """
        # this is a backward-compatible default
        return {name: ShapeSpec(channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]) for
                name in self._out_features
                }


# def mobilenet_v2(pretrained=False, progress=True, **kwargs):
#     """
#     Constructs a MobileNetV2 architecture from
#     `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
#
#     Args:
#         pretrained (bool): If True, returns a model pre-trained on ImageNet
#         progress (bool): If True, displays a progress bar of the download to stderr
#     """
#     model = MobileNetV2(**kwargs)
#     if pretrained:
#         state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
#                                               progress=progress)
#         model.load_state_dict(state_dict)
#     return model


@BACKBONE_REGISTRY.register()
def build_mobilenetv2_backbone(cfg, input_shape):
    """
    Create a MobileNetV2 instance from config.

    Returns:
        MobileNetV2: a :class:`MobileNetV2` instance.
    """
    out_features = cfg.MODEL.MOBILE_NET_V2.OUT_FEATURES
    model = MobileNetV2(out_features=out_features)
    return model
